Search Results (77)

Sweat-based electrochemical sensors for wearable applications have attracted substantial interest due to their non-invasive nature, compact design, and ability to provide real-time data. Remarkable advancements have been made in integrating these devices into flexible platforms. While thin-film polymer substrates are frequently employed for their durability, the prolonged buildup of sweat on such materials can disrupt consistent sensing performance and adversely affect skin comfort over extended periods. Therefore, investigating lightweight, comfortable, and breathable base materials for constructing working electrodes is essential for producing flexible and breathable sweat electrochemical sensors. In this study, nylon fabric was chosen as the base material for constructing the working electrode. The electrode is prepared using a straightforward printing process, incorporating Ti₃C₂T_X MXene/polyaniline and methylene blue as modification materials in the electronic intermediary layer. The synergistic effect of the modified layer and the multi-level structure of the current collector enhances the electrochemical kinetics on the electrode surface, improves electron transmission efficiency, and enables the nylon fabric-based electrode to accurately and selectively measure glucose concentration in sweat. It exhibits a wide linear range (0.04~3.08 mM), high sensitivity (3.11 μA·mM⁻¹), strong anti-interference capabilities, and high stability. This system can monitor glucose levels and trends in sweat, facilitating the assessment of daily sugar intake for personal health management. Full article

Metabolic diseases are increasing in relevance both in health and the economy in most countries. In this direction, if gold-standard technologies are based on blood analysis, non-invasive glucose monitoring is a relevant and great challenge that has not yet been fully resolved. Sweat represents a more suitable medium for the non-invasive sensing and monitoring of glucose than other bodily fluids, such as saliva, tears, or urine. However, the measurement of glucose levels requires the use of highly precise and sensitive sensors, given the low glucose concentration in sweat. This paper provides an overview of the patent landscape related to wearable biosensors for the monitoring of glucose levels in sweat. Full article

Optical clearing agents (OCAs) offer a promising approach to enhance skin transparency by reducing scattering and improving photon transmission, which is critical for non-invasive optical diagnostics such as glucose sensing and vascular imaging. However, the complex multilayered structure of skin and anatomical variability across different regions pose challenges for accurately evaluating OCA performance. In this study, we developed a multilayer Monte Carlo (MC) simulation model integrated with a depth- and time-resolved diffusion model based on Fick’s law to quantitatively assess the combined effects of OCA penetration depth and refractive index change on optical clearing. The model incorporates realistic skin parameters, including variable stratum corneum thicknesses, and was validated through in vivo experiments using glycerol and glucose at different concentrations. Both the simulation and experimental results demonstrate that increased stratum corneum thickness significantly reduces blood absorption of light and lowers the clearing efficiency of OCAs. The primary influence of stratum corneum thickness lies in requiring a greater degree of refractive index matching rather than necessitating a deeper OCA penetration depth to achieve effective optical clearing. These findings underscore the importance of considering regional skin differences when selecting OCAs and designing treatment protocols. This work provides quantitative insights into the interaction between tissue structure and optical response, supporting improved application strategies in clinical diagnostics. Full article

Mid-infrared passive spectroscopic imaging is a novel non-invasive and remote sensing method based on Planck’s law. It enables the acquisition of component-specific information from the human body by measuring naturally emitted thermal radiation in the mid-infrared region. Unlike active methods that require an external light source, our passive approach harnesses the body’s own emission, thereby enabling safe, long-term monitoring. In this study, we successfully demonstrated the simultaneous, non-invasive measurements of blood glucose and lactate levels of the human body using this method. The measurements, conducted over approximately 80 min, provided emittance data derived from mid-infrared passive spectroscopy that showed a temporal correlation with values obtained using conventional blood collection sensors. Furthermore, to evaluate localized metabolic changes, we performed k-means clustering analysis of the spectral data obtained from the upper arm. This enabled visualization of time-dependent lactate responses with spatial resolution. These results demonstrate the feasibility of multi-component monitoring without physical contact or biological sampling. The proposed technique holds promise for translation to medical diagnostics, continuous health monitoring, and sports medicine, in addition to facilitating the development of next-generation healthcare technologies. Full article

In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate using borohydride reduction, ensuring uniform distribution. The optimized SERS substrates exhibited a high enhancement factor (EF) of up to 10⁵ for the detection of 4-mercaptobenzoic acid (4-MBA). To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). These results highlight the potential of AgNP-modified substrates for reliable and reusable biochemical sensing applications. Full article

This study investigates a noninvasive continuous glucose monitoring (NI-CGM) system optimized for earlobe application, leveraging the site’s anatomical advantages—absence of bone, muscle, and thick skin—for enhanced optical transmission. The system integrates multimodal sensing, combining near-infrared (NIR) diffuse transmission with temperature and pressure sensors. A novel Multi-Wavelength Slope Efficiency Near-Infrared Spectroscopy (MW-SE-NIRS) method is introduced, enhancing noise robustness through the slope efficiency-based parameterization of NIR signal dynamics. By employing three NIR wavelengths with distinct scattering and absorption properties, the method improves glucose detection reliability, addressing tissue heterogeneity and physiological noise in noninvasive monitoring. To validate the feasibility, a pilot clinical trial enrolled five participants with normal or pre-diabetic glucose profiles. Continuous glucose data capturing pre- and postprandial variations were analyzed using a 1D convolutional neural network (Conv1D). For three subjects under stable physiological conditions, the model achieved 97.0% Clarke error grid (CEG) A-Zone accuracy and a mean absolute relative difference (MARD) of 5.2%. Across all participants, results showed 90.9% CEG A-Zone accuracy and a MARD of 8.4%, with performance variations linked to individual factors such as earlobe thickness variability and physical activity. These outcomes demonstrate the potential of the MW-SE-NIRS system for noninvasive glucose monitoring and highlight the importance of future work on personalized modeling, sensor optimization, and larger-scale clinical validation. Full article

This paper presents the design of an orientation-independent non-invasive glucose sensor based on a meta-structured antenna. The sensor is designed for blood glucose measurement through fingertip placement on the sensor and features a mushroom structure to generate zeroth-order resonance (ZOR). Moreover, the mushroom structure has a hexagonal patch for orientation-independent non-invasive sensing. The operating frequency of the sensor is 4 GHz, and the overall size is 55 mm × 55 mm. In our study, the range of glucose concentration is from 50 to 250 mg/dL, with a step size of 50 mg/dL. The simulated and measured results show a linear relationship between the resonance frequency and the glucose concentration in the solution, and the linear shift of 0.352 MHz/mg/dL has been observed. On the other hand, the reflection coefficient level variation is a nonlinear function of the glucose concentration for the considered concentration ranges. Mathematical models describing the sensor response across all fingertip orientations are developed for the designed sensor using the regression analysis (R² ≥ 0.993) relating the glucose concentration to the measured resonance frequency and reflection coefficient level. While the reflection coefficient shows a nonlinear response, the resonance frequency exhibits a strong linear correlation with glucose concentration, making it a more reliable parameter for accurate prediction in the proposed sensing model. Full article

Sweat analysis represents an emerging non-invasive approach for health monitoring, yet its practical application is hindered by challenges such as insufficient natural sweat secretion and inefficient collection. To overcome these limitations, this study develops a hydrogel sheet composed of agarose and glycerol, which efficiently facilitates resting sweat collection without external stimulation when integrated into the microfluidic channels of a sweat-sensing patch. The microfluidic sweat-sensing patch, fabricated with laser-cut technology, features a sandwich structure that enables the measurement of sweat rate and chloride ion concentration while minimizing interference from electrochemical reactions. Additionally, a colorimetric module utilizing glucose oxidase and peroxidase is also integrated into the platform for cost-effective and efficient glucose detection through a color change that can be quantified via RGB analysis. The hydrogel interface, characterized by its optimal thickness and water content, exhibits superior absorption capability for efficient sweat collection and retention, with a negligible effect on the dilution of sweat components. This hydrogel-interfaced microfluidic platform demonstrates high efficiency in sweat collection and multi-biomarker analysis, offering a non-invasive, real-time solution for health monitoring. Its low-cost and wearable design highlights its potential for broad applications in personalized healthcare. Full article

An optimized design of photodetectors and antennas for Förster Resonance Energy Transfer (FRET)-based glucose biosensing in endoscopic capsules is presented. The compact antenna design is tailored for the visible optical frequencies (~526 THz) associated with FRET-based glucose monitoring and integrates structural flexibility to conform to the spatial constraints of endoscopic capsules, such as mechanical bending features. The antenna is embedded in a multimode medium artificial tissue simulating a glucose environment with several layers, providing efficient coupling to the FRET emission signal for glucose sensing. Stable S11 parameters and a maximum gain of 9 dBi are realized by statelier mesh settings, bend adaptation, and cautious SAR constraint handlers. Results of the Specific Absorption Rate (SAR) confirm the limited energy absorption within permissible bounds, confirming its application for biomedical purposes. These results affirm the feasibility of non-invasive glucose measurement in interstitial fluid in this configuration that can be operable through an endoscope with improved sensitivity and functionality. Full article

Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring. Full article

This review highlights the latest developments in wearable electrochemical glucose sensors, focusing on their transition from invasive to non-invasive and minimally invasive designs. We discuss the underlying mechanisms, performance metrics, and practical challenges of these technologies, emphasizing their potential to revolutionize diabetes care. Additionally, we explore the motivation behind this review: to provide a comprehensive analysis of emerging sensing platforms, assess their clinical applicability, and identify key research gaps that need addressing to achieve reliable, long-term glucose monitoring. By evaluating electrochemical sensors based on tears, saliva, sweat, urine, and interstitial fluid, this work aims to guide future innovations toward more accessible, accurate, and user-friendly solutions for diabetic patients, ultimately improving their quality of life and disease management outcomes. Full article

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna’s sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management. Full article

Benchmark data are reported for a solid-state laser-based near-infrared spectrometer designed for noninvasive measurements in human skin. These data were obtained using a set of aqueous phantoms composed of polystyrene beads, triton X-100, saline, and glucose. The performance of this prototype solid-state laser platform was compared to parallel results obtained with a Fourier-transform (FT) spectrometer. The fundamental spectroscopic performances of the two spectrometer systems were quantified by an analysis of 100% lines determined by ratioing back-to-back spectra collected over time for each phantom. Root mean square (RMS) noise levels were computed for each dataset and the median RMS noise levels were 327.8 µAU and 667.2 µAU for the FT spectrometer and prototype laser platform, respectively. The analytical utility of the solid-state laser platform was assessed through a series of leave-one-phantom-out partial least squares analyses. Results for the laser prototype data included a standard error of cross validation (SECV) of 7.82 mg/dL for an optimized PLS model with 10 factors over a spectral range of 1401–2238 nm. This compares favorably with the results from the FT spectrometer of an SECV of 6.62 mg/dL with 8 factors and a spectral range of 1551–2378 nm. The additional two PLS factors for the laser prototype were shown to be a consequence of its higher spectral noise. Selectivity of these PLS models was demonstrated by comparing models associated with correct and random glucose assignments to each spectrum. Overall, these findings benchmark the analytical utility of this solid-state laser prototype. Full article

Accurate in vivo glucose monitoring is essential for effective diabetes management and for the care of pre-term infants in critical care. Glucose-monitoring techniques are broadly categorized into three types: invasive, minimally invasive, and non-invasive. Each method presents distinct advantages and challenges. Non-invasive glucose sensors, despite impressive advancements in recent years, still face issues with signal interference and accuracy, limiting their widespread clinical application. In contrast, implanted devices offer more reliable and consistent results in clinical settings, making them the current gold standard. This review provides an overview of the leading glucose-sensing technologies, detailing both their advantages and drawbacks. We discuss invasive techniques, such as implanted electrodes, which allow continuous glucose monitoring with high accuracy, but often come with risks of infection and discomfort. Minimally invasive methods, such as fluorescence sensors, Raman sensors, and microneedle arrays, aim to reduce discomfort while providing more precise measurements than non-invasive devices. Additionally, non-invasive methods, such as optical, infrared, and microwave techniques, are explored for their potential to provide pain-free, continuous glucose monitoring. Finally, the review highlights a brief comparison among the current technologies and future directions in the field, particularly the use of signal enhancement algorithms and integration with wearable devices. Full article

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents’ overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in $R^2$, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context. Full article